Literature DB >> 30429286

The Length Distribution and Multiple Specificity of Naturally Presented HLA-I Ligands.

David Gfeller1,2, Philippe Guillaume3, Justine Michaux3,4, Hui-Song Pak3,4, Roy T Daniel5, Julien Racle3,2, George Coukos3,4, Michal Bassani-Sternberg3,4.   

Abstract

HLA-I molecules bind short peptides and present them for recognition by CD8+ T cells. The length of HLA-I ligands typically ranges from 8 to 12 aa, but variability is observed across different HLA-I alleles. In this study we collected recent in-depth HLA peptidomics data, including 12 newly generated HLA peptidomes (31,896 unique peptides) from human meningioma samples, to analyze the peptide length distribution and multiple specificity across 84 different HLA-I alleles. We observed a clear clustering of HLA-I alleles with distinct peptide length distributions, which enabled us to study the structural basis of peptide length distributions and predict peptide length distributions from HLA-I sequences. We further identified multiple specificity in several HLA-I molecules and validated these observations with binding assays. Explicitly modeling peptide length distribution and multiple specificity improved predictions of naturally presented HLA-I ligands, as demonstrated in an independent benchmarking based on the new human meningioma samples.
Copyright © 2018 by The American Association of Immunologists, Inc.

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Year:  2018        PMID: 30429286     DOI: 10.4049/jimmunol.1800914

Source DB:  PubMed          Journal:  J Immunol        ISSN: 0022-1767            Impact factor:   5.422


  47 in total

1.  A comprehensive review and performance evaluation of bioinformatics tools for HLA class I peptide-binding prediction.

Authors:  Shutao Mei; Fuyi Li; André Leier; Tatiana T Marquez-Lago; Kailin Giam; Nathan P Croft; Tatsuya Akutsu; A Ian Smith; Jian Li; Jamie Rossjohn; Anthony W Purcell; Jiangning Song
Journal:  Brief Bioinform       Date:  2020-07-15       Impact factor: 11.622

2.  Mass Spectrometry Based Immunopeptidomics Leads to Robust Predictions of Phosphorylated HLA Class I Ligands.

Authors:  Marthe Solleder; Philippe Guillaume; Julien Racle; Justine Michaux; Hui-Song Pak; Markus Müller; George Coukos; Michal Bassani-Sternberg; David Gfeller
Journal:  Mol Cell Proteomics       Date:  2019-12-17       Impact factor: 5.911

3.  A machine learning model for ranking candidate HLA class I neoantigens based on known neoepitopes from multiple human tumor types.

Authors:  Jared J Gartner; Maria R Parkhurst; Alena Gros; Eric Tran; Mohammad S Jafferji; Amy Copeland; Ken-Ichi Hanada; Nikolaos Zacharakis; Almin Lalani; Sri Krishna; Abraham Sachs; Todd D Prickett; Yong F Li; Maria Florentin; Scott Kivitz; Samuel C Chatmon; Steven A Rosenberg; Paul F Robbins
Journal:  Nat Cancer       Date:  2021-05-03

4.  Robust prediction of HLA class II epitopes by deep motif deconvolution of immunopeptidomes.

Authors:  Julien Racle; Justine Michaux; Georg Alexander Rockinger; Marion Arnaud; Sara Bobisse; Chloe Chong; Philippe Guillaume; George Coukos; Alexandre Harari; Camilla Jandus; Michal Bassani-Sternberg; David Gfeller
Journal:  Nat Biotechnol       Date:  2019-10-14       Impact factor: 54.908

5.  The immunopeptidomes of two transmissible cancers and their host have a common, dominant peptide motif.

Authors:  Annalisa Gastaldello; Sri H Ramarathinam; Alistair Bailey; Rachel Owen; Steven Turner; N Kontouli; Tim Elliott; Paul Skipp; Anthony W Purcell; Hannah V Siddle
Journal:  Immunology       Date:  2021-02-04       Impact factor: 7.397

Review 6.  Next-generation computational tools for interrogating cancer immunity.

Authors:  Francesca Finotello; Dietmar Rieder; Hubert Hackl; Zlatko Trajanoski
Journal:  Nat Rev Genet       Date:  2019-09-12       Impact factor: 59.581

7.  High-Throughput Prediction of MHC Class I and II Neoantigens with MHCnuggets.

Authors:  Xiaoshan M Shao; Rohit Bhattacharya; Justin Huang; I K Ashok Sivakumar; Collin Tokheim; Lily Zheng; Dylan Hirsch; Benjamin Kaminow; Ashton Omdahl; Maria Bonsack; Angelika B Riemer; Victor E Velculescu; Valsamo Anagnostou; Kymberleigh A Pagel; Rachel Karchin
Journal:  Cancer Immunol Res       Date:  2019-12-23       Impact factor: 12.020

8.  Predicting MHC class I binder: existing approaches and a novel recurrent neural network solution.

Authors:  Limin Jiang; Hui Yu; Jiawei Li; Jijun Tang; Yan Guo; Fei Guo
Journal:  Brief Bioinform       Date:  2021-11-05       Impact factor: 13.994

Review 9.  Bioinformatic HLA Studies in the Context of SARS-CoV-2 Pandemic and Review on Association of HLA Alleles with Preexisting Medical Conditions.

Authors:  Mina Mobini Kesheh; Sara Shavandi; Parastoo Hosseini; Rezvan Kakavand-Ghalehnoei; Hossein Keyvani
Journal:  Biomed Res Int       Date:  2021-05-28       Impact factor: 3.411

10.  Challenges targeting cancer neoantigens in 2021: a systematic literature review.

Authors:  Ina Chen; Michael Y Chen; S Peter Goedegebuure; William E Gillanders
Journal:  Expert Rev Vaccines       Date:  2021-06-09       Impact factor: 5.683

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